🤖 AI Analysis

Final verdict: SAFE

The package shows very low risk across all assessed categories with no signs of malicious activity or obfuscation. The metadata suggests it is new and has not yet gained significant traction, but there are no red flags.

No network calls
No shell execution
No obfuscation
No credential harvesting
Minimal engagement

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
Metadata: The package appears to be newly created with minimal engagement, but no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3103 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

8 type-annotated function signatures (partial)

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 18 commits in stfc/alc-aiidalab-widgets
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: stfc.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Dr. Benjamin T. Speake" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with alc-aiidalab-widgets

Create a mini-application called 'Material Explorer' that leverages the 'alc-aiidalab-widgets' package to allow users to explore and analyze materials data. This application should provide a user-friendly interface for selecting materials from a database, viewing their properties, and running basic analyses on them. Here are the steps and features you should include:

1. **Setup**: Begin by installing the necessary packages including 'alc-aiidalab-widgets'. Ensure that your development environment is set up to use these widgets effectively.
2. **Database Integration**: Integrate a backend service that provides access to a materials database. This could be a local SQLite database or a remote API service like Materials Project.
3. **User Interface**: Use 'alc-aiidalab-widgets' to create a clean and interactive UI where users can search for materials by name, formula, or ID. The widgets should facilitate easy navigation and selection of materials.
4. **Material Properties Display**: Once a material is selected, display its key properties such as crystal structure, electronic band gap, and density using 'alc-aiidalab-widgets'. These properties should be visualized in a way that is both informative and visually appealing.
5. **Analysis Tools**: Implement simple analysis tools within the application. For example, allow users to calculate the thermal conductivity based on the provided data or visualize the electronic band structure.
6. **Customization Options**: Provide options for users to customize the visualization of the material properties. They should be able to choose between different plot types, color schemes, etc.
7. **Documentation and Help**: Include comprehensive documentation and tooltips within the application to guide users through its features and functionalities.
8. **Testing and Validation**: Ensure that all features work as expected by thoroughly testing the application. Validate the accuracy of the displayed information and the functionality of the analysis tools.
9. **Deployment**: Prepare the application for deployment. Decide whether it will be a standalone web app or integrated into an existing AiiDAlab environment. Ensure it is accessible and user-friendly for the target audience.

This project not only showcases the capabilities of 'alc-aiidalab-widgets' but also demonstrates how to integrate complex scientific data into a user-friendly application.